Transportationer — 15-Minute City Analyzer

A web application for analyzing urban accessibility through the lens of the 15-minute city concept. Shows a heatmap indicating distance to locations of interest across 5 categories: Service & Trade, Transport, Work & School, Culture & Community, and Recreation.

Architecture

Browser (Next.js / React)
  ├── MapLibre GL JS (map + canvas heatmap / isochrone overlay)
  └── API calls → Next.js API routes

Next.js App Server
  ├── Public API: /api/cities /api/tiles /api/stats /api/location-score /api/isochrones
  ├── Admin API: /api/admin/** (auth-protected)
  ├── PostgreSQL + PostGIS (POIs, grid points, precomputed scores)
  └── Valkey (API response cache, BullMQ queues)

BullMQ Worker (download queue, concurrency 1)
  └── download-pbf      → streams OSM PBF from Geofabrik; cached by URL basename
                          (multiple cities sharing the same regional PBF download it once),
                          24 h TTL, serialised to avoid redundant parallel downloads

BullMQ Worker (pipeline queue, concurrency 8)
  ├── refresh-city      → orchestrates full ingest via FlowProducer
  ├── extract-pois      → osmium filter + osm2pgsql flex → raw_pois
  ├── generate-grid     → PostGIS 200 m rectangular grid → grid_points
  ├── compute-scores    → two-phase orchestrator (see Scoring below)
  ├── compute-routing   → dispatches matrix job to 'routing' queue → grid_poi_details
  │                        (15 parallel jobs: 3 modes × 5 categories)
  └── compute-transit   → dispatches isochrone jobs to 'routing-transit' queue →
                           grid_poi_details (travel_mode='transit')
                           (1 job per city, covers all categories via PostGIS spatial join)

BullMQ Worker (valhalla queue, concurrency 1) — road-only tile builder + router
  ├── build-valhalla   → osmium clip + valhalla_build_tiles (road graph only, no transit
  │                       connections per city tile dir VALHALLA_TILES_BASE/{citySlug}/)
  │                       Clean tiles ensure cycling/walking/driving routing is never
  │                       affected by ghost edges from failed transit connections.
  └── routing queue    → @valhallajs/valhallajs Actor pool (per-city, lazy-loaded)
       ├── matrix jobs (walking/cycling/driving) ← compute-routing
       └── isochrone jobs (non-transit)          ← /api/isochrones

BullMQ Worker (valhalla-transit queue, concurrency 1) — transit tile builder + router
  ├── download-gtfs-de → raw feed cached globally in gtfs/raw/ (keyed by source URL);
  │                       per-city filtered feed in gtfs/{citySlug}/feed/ (bbox-clipped,
  │                       trips with <2 bbox stops removed); 24 h source TTL
  ├── build-valhalla   → osmium clip (expanded to transit stops bbox) +
  │                       valhalla_ingest_transit + valhalla_convert_transit +
  │                       valhalla_build_tiles (road graph with transit connections)
  │                       per city tile dir VALHALLA_TILES_BASE/{citySlug}/
  └── routing-transit queue → @valhallajs/valhallajs Actor pool (per-city, lazy-loaded)
       ├── isochrone jobs (multimodal) ← compute-transit
       └── isochrone jobs (transit)    ← /api/isochrones

Protomaps → self-hosted map tiles (PMTiles)

Quick Start

1. Configure environment

cp .env.example .env
# Edit .env with strong passwords

# Generate admin password hash
node -e "require('bcryptjs').hash('yourpassword', 12).then(console.log)"
# Paste result as ADMIN_PASSWORD_HASH in .env

2. Start services

docker compose up -d

3. Add a city

Open http://localhost:3000/admin, log in, click Add City, browse Geofabrik regions (e.g. europe/germany/berlin), and start ingestion. Progress is shown live.

Processing time:

• Small city (< 100k pop): ~5–15 minutes
• Large city (1M+ pop): ~30–90 minutes

4. Explore

Open http://localhost:3000 and select your city.

Map Tiles

By default the app uses CartoDB Positron (CDN). For fully offline operation, download a PMTiles file for your region:

# Example: download Berlin region tiles
wget https://maps.protomaps.com/builds/berlin.pmtiles -O apps/web/public/tiles/region.pmtiles
# Then switch to the PMTiles style:
cp apps/web/public/tiles/style.pmtiles.json apps/web/public/tiles/style.json

Development

npm install
npm run dev               # Next.js dev server on :3000
npm run worker:dev        # BullMQ worker with hot reload

Required local services: PostgreSQL+PostGIS, Valkey. Easiest via:

docker compose up postgres valkey -d

Methodology

The 15-minute city concept holds that a liveable urban neighbourhood is one where daily necessities are reachable within 15 minutes on foot or by bike. Transportationer operationalises this as a continuous, queryable accessibility score for every location in a city.

What is measured

For each location (grid point) the tool asks: how quickly can you reach the nearest representative destination in each category, by each travel mode? The answer is a travel time in seconds, obtained from real routing data rather than straight-line distance.

Destinations are sourced from OpenStreetMap and grouped into five categories — Service & Trade, Transport, Work & School, Culture & Community, and Recreation — each split into subcategories (e.g. supermarket, pharmacy, cafe). Multiple OSM tags may map to the same subcategory (e.g. shop=bakery and amenity=cafe both map to cafe).

Grid

Each city is covered by a regular rectangular grid at 200 m spacing, generated in Web Mercator (EPSG:3857) and projected back to WGS84. One score is stored per grid point per (category × travel mode × threshold × profile) combination. On the map each grid point is rendered as a circle.

Routing

Travel times are obtained from Valhalla, a real-network routing engine built on OSM data:

• Walking, cycling, driving — Valhalla's sources_to_targets matrix endpoint. For each grid point the 6 spatially nearest POIs (by KNN) in the category are sent as targets; the resulting travel-time matrix gives the exact routed time to each. The fastest-routed POI per subcategory is retained.
• Transit — Valhalla's matrix endpoint does not support transit. Instead, a multimodal isochrone is computed per grid point at contour intervals of 5, 10, 15, 20, and 30 minutes (fixed departure: next Tuesday 08:00 for reproducible GTFS results). PostGIS ST_Within then classifies every POI in the city into the smallest contour it falls within, giving estimated times of 300 / 600 / 900 / 1200 / 1800 seconds. Grid points outside the transit network are silently skipped — they receive no transit score.
• Cyclist (cyclist) — synthetic persona: MIN(walking, cycling, transit) per POI. Represents someone who cycles and also uses transit when faster. No extra routing calls needed.
• Cyclist, no transit (cycling_walk) — synthetic persona: MIN(walking, cycling). Represents someone who cycles but avoids public transit. No extra routing calls needed.
• Transit + Walk (transit_walk) — synthetic persona: MIN(walking, transit). Represents someone who does not cycle. No extra routing calls needed.
• Walker — the raw walking mode also serves as a fourth persona: someone who only walks.

Scoring formula

All scores are precomputed at ingest time for every combination of threshold (5 / 10 / 15 / 20 / 30 min), travel mode, and profile, so interactive queries hit only the database.

Each subcategory i contributes a proximity score based on travel time t and threshold T (both in seconds) using exponential decay:

score(t, T) = exp(−t / T)

At t = 0 the score is 1.0. At the threshold it is exp(−1) ≈ 0.37 — a POI reachable in exactly the threshold time still contributes meaningfully. A third of the threshold away scores ~0.72, halfway scores ~0.61. The curve rewards proximity without harshly penalising destinations that are merely a bit further than ideal.

The category score aggregates across subcategories and across multiple nearby POIs of the same subcategory via a complement product weighted by profile-specific importance weights w_i ∈ [0, 1]:

category_score = 1 − ∏ (1 − w_i × score(t_i, T))

This captures both subcategory coverage (a pharmacy and a supermarket together score higher than either alone) and within-subcategory diversity (a second nearby park still improves the score, with strongly diminishing returns). Subcategories with no POI found contribute nothing and do not penalise the score.

The composite score shown on the heatmap is a weighted average of all five category scores. Category weights come from the selected profile but can be adjusted freely with the UI sliders. Changing the profile, threshold, or travel mode re-queries the database; adjusting the sliders re-blends client-side with no server round-trip.

Category Definitions

The five categories and their subcategories are defined below. All categories are scored at the same user-selected threshold. The universal weight column shows the subcategory importance weight for the Universal profile (range 0–1); other profiles override specific values — see the Profiles table in the Scoring section.

Service & Trade

OSM tag(s)	Subcategory	Universal weight
shop=supermarket, shop=wholesale	supermarket	1.0
shop=convenience	convenience	0.65
amenity=pharmacy, shop=pharmacy	pharmacy	1.0
amenity=restaurant, amenity=fast_food	restaurant	0.55
amenity=cafe, shop=bakery	cafe	0.4
amenity=bank	bank	0.35
amenity=post_office	post_office	0.4
shop=greengrocer, shop=butcher, amenity=marketplace, shop=department_store, shop=mall	market	0.4
shop=laundry, shop=dry_cleaning	laundry	0.3
amenity=atm	atm	0.2

Transport

OSM tag(s)	Subcategory	Universal weight
railway=station, railway=halt	train_station	1.0
railway=subway_entrance, railway=subway_station	metro	1.0
railway=tram_stop	tram_stop	0.75
highway=bus_stop, amenity=bus_station	bus_stop	0.55
amenity=ferry_terminal	ferry	0.5
amenity=bicycle_rental	bike_share	0.5
amenity=car_sharing	car_share	0.5

Excluded: public_transport=stop_position and public_transport=platform — these duplicate highway=bus_stop / railway=tram_stop nodes at the exact same location and would double-count stops in scoring.

Work & School

OSM tag(s)	Subcategory	Universal weight
amenity=kindergarten, amenity=childcare	kindergarten	0.75
amenity=school	school	0.7
office=coworking	coworking	0.55
amenity=university, amenity=college	university	0.55
amenity=driving_school	driving_school	0.2

Excluded: office=company, office=government, landuse=commercial, landuse=office — ubiquitous in every urban block; they always win the "nearest POI" race in the detail view, masking meaningful destinations. Government buildings are captured via amenity=townhall / amenity=police in Culture & Community instead.

Culture & Community

OSM tag(s)	Subcategory	Universal weight
amenity=hospital	hospital	1.0
amenity=clinic, amenity=doctors	clinic	0.8
amenity=library	library	0.7
amenity=community_centre, leisure=arts_centre	community_center	0.6
amenity=social_facility	social_services	0.6
amenity=theatre, amenity=cinema	theatre	0.5
tourism=museum	museum	0.4
amenity=townhall, amenity=police	government	0.4
amenity=place_of_worship	place_of_worship	0.25

Recreation

OSM tag(s)	Subcategory	Universal weight
leisure=park, leisure=garden	park	1.0
leisure=playground	playground	0.85
leisure=sports_centre, leisure=pitch	sports_facility	0.65
leisure=fitness_centre	gym	0.65
leisure=nature_reserve, leisure=golf_course, landuse=recreation_ground, landuse=grass, landuse=meadow, landuse=forest	green_space	0.6
leisure=swimming_pool, amenity=swimming_pool	swimming_pool	0.55

Profiles

Each profile carries two sets of weights:

• Category weights (slider presets in the UI, range 0–2): relative importance of each category in the composite score.
• Subcategory weights (baked into precomputed scores, range 0–1): how strongly a specific subcategory contributes to its parent category score. Any subcategory not listed in a profile falls back to 0.5.

Profile	Emoji	Category weights	Notable subcategory overrides (vs. Universal)
Universal	⚖️	All 1.0	Baseline — see category tables above for all weights
Young Family	👨‍👩‍👧	Work & School 1.5, Recreation 1.4, Service 1.2, Culture 0.9, Transport 1.0	school → 1.0, kindergarten → 1.0, playground → 1.0, clinic → 1.0, park → 1.0; gym → 0.5, university → 0.2
Senior	🧓	Culture & Community 1.5, Service 1.4, Transport 1.1, Recreation 1.0, Work & School 0.3	hospital → 1.0, clinic → 1.0, pharmacy → 1.0, social_services → 0.9, bus_stop → 0.75, tram_stop → 0.8, metro → 0.8; school → 0.05, kindergarten → 0.05, university → 0.15
Young Professional	💼	Transport 1.5, Recreation 1.1, Service 1.0, Culture 0.9, Work & School 0.7	metro → 1.0, train_station → 1.0, tram_stop → 0.85, bike_share → 0.7; gym → 0.9, restaurant → 0.75, coworking → 0.85; school → 0.1, kindergarten → 0.05
Student	🎓	Work & School 1.5, Transport 1.4, Culture & Community 1.2, Service 0.9, Recreation 0.8	university → 1.0, library → 1.0, coworking → 0.9, bike_share → 0.85, cafe → 0.9, metro → 1.0, train_station → 0.9; school → 0.05, kindergarten → 0.05

Implementation Details

Data pipeline

For each city the worker pipeline runs in two phases:

Phase 1 — Routing (parallel child jobs, dispatched by compute-scores)

• Walking, cycling, driving — 15 parallel jobs (3 modes × 5 categories). A PostGIS KNN lateral join finds the 6 spatially nearest POIs per grid point in the category; those coordinates are dispatched as a matrix job to the routing BullMQ queue, where the valhalla worker's Actor pool runs sources_to_targets. The nearest POI per subcategory is persisted to grid_poi_details.
• Transit — 1 job per city (compute-transit). Concurrent isochrone jobs (8 at a time) dispatched to the routing-transit queue; the transit valhalla worker's Actor pool runs multimodal isochrones and PostGIS ST_Within classifies POIs into contour bands. Runs first so it overlaps with the routing jobs.

Phase 2 — Score aggregation

A single SQL CTE chain inside PostgreSQL computes all scores without streaming data through Node.js. Precomputed for every combination of 5 thresholds × 5 travel modes × 5 profiles, then bulk-inserted into grid_scores via ON CONFLICT DO UPDATE.

Travel modes

Mode	Key	Source
Cyclist	cyclist	Synthetic — MIN(walking, cycling, transit) per POI. Persona: cycles + uses transit.
Cyclist, no transit	cycling_walk	Synthetic — MIN(walking, cycling) per POI. Persona: cycles, avoids transit.
Transit + Walk	transit_walk	Synthetic — MIN(walking, transit) per POI. Persona: does not cycle.
Walking	walking	Valhalla pedestrian matrix, exact seconds
Cycling	cycling	Valhalla bicycle matrix, exact seconds
Transit	transit	Valhalla multimodal isochrone, quantised to 5-min bands (requires GTFS feed)
Driving	driving	Valhalla auto matrix, exact seconds

Per-location score (pin)

When a user places a pin on the map:

1. The nearest grid point is found via a PostGIS <-> KNN query.
2. Precomputed grid_scores rows for that grid point, travel mode, threshold, and profile are returned — one row per category.
3. Per-subcategory detail rows from grid_poi_details are also fetched, showing the name, straight-line distance, and travel time to the fastest-routed POI in each subcategory for the requested mode. The category headline time shown in the panel is the minimum across all subcategories.
4. An isochrone overlay is dispatched via BullMQ and computed by the valhalla worker's Actor pool. For transit mode the job goes to the routing-transit queue (multimodal Actor). For fifteen (Best mode), cycling is used as the representative display isochrone since Valhalla's isochrone only supports single-mode costing.

The pin panel also shows estate value data (land price in €/m² from the BORIS NI cadastre) for cities in Lower Saxony, including a percentile rank among all zones in the city and a "peer percentile" rank among zones with similar accessibility scores.

Hidden gem score

For cities with BORIS NI estate value data, a hidden gem score is precomputed per grid point at the end of Phase 2:

hidden_gem_score = composite_accessibility × (1 − price_rank_within_decile)

• composite_accessibility — average of all category scores for that grid point (walking / 15 min / universal profile)
• price_rank_within_decile — PERCENT_RANK() of the nearest zone's land price among all zones in the same accessibility decile (0 = cheapest, 1 = most expensive relative to equally accessible peers)

The result is in [0, 1]: high only when a location is both accessible and priced below its peers. Stored in grid_points.hidden_gem_score and served as a separate MVT overlay at /api/tiles/hidden-gems/.

The map offers three mutually exclusive base overlays (switchable in the control panel):

• Accessibility — default grid heatmap coloured by composite score
• Land value — BORIS NI zones coloured by €/m² (Lower Saxony cities only)
• Hidden gems — grid points coloured by hidden gem score (Lower Saxony cities only)